Model framework
The choice of model framework affects model complexity. The factor of model framework includes model type (e.g., FCNN, CNN), activation function (e.g., Sigmoid [1]}, ReLU [2]}), and others. Different model frameworks may require different complexity measure criteria and may not be directly comparable to each other.
Model size
The size of deep model affects model complexity. Some commonly used measures of model size include the number of parameters, the number of hidden layers, the width of hidden layers, the number of filters, and the filter size. Under the same model framework, the complexities of models with different sizes can be quantified by the same complexity measure criteria and thus become comparable [3]}.
Optimization process
The optimization process affects model complexity, including the form of objective functions, the selection of learning algorithms, and the setting of hyperparameters.
Data complexity
The data on which a model is trained affect model complexity, too. The major factors include the data dimensionality, data distribution [4]}, information volume measured by Kolmogorov complexity [5]}, [6]}, and some others.

